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CONSTRUCTION INDUSTRY INSTITUTE
THE EFFECTS OF CHANGES ON LABOR PRODUCTIVITY: WHY AND HOW MUCH
The Pennsylvania State University H. Randolph Thomas
Carmen L. Napolitan
Source Document 99 August 1994
ÚÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄ¿ ³ CII Application Notes: ³³ ³³ 1. Character(s) preceded & followed by these symbols (À Ù) or (Ú ¿) ³³ are super- or subscripted, respectively. ³³ EXAMPLES: 42mÀ3Ù = 42 cubic meters ³³ COÚ2¿ = carbon dioxide ³³ ³³ 2. All degree symbols have been replaced with the word deg. ³³ ³³ 3. All plus or minus symbols have been replaced with the symbol +/-. ³³ ³³ 4. All table note letters and numbers have been enclosed in square ³³ brackets in both the table and below the table. ³³ ³³ 5. Whenever possible, mathematical symbols have been replaced with ³³ their proper name and enclosed in square brackets. ³ÀÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÄÙ
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THE EFFECTS OF CHANGES ON LABOR PRODUCTIVITY: WHY AND HOW MUCH
by H. Randolph Thomas
and Carmen L. Napolitan
A Report to The Construction Industry Institute The University of Texas at Austin
Under the Guidance of the Change Order Impacts Task Force
from The Pennsylvania State University
University Park, Pennsylvania
August 1994
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TABLE OF CONTENTS
Chapter
EXECUTIVE SUMMARY ... v
1. INTRODUCTION ... 1 OBJECTIVES ... 1SCOPE ... 1DEFINITIONS ... 1
2. BACKGROUND ... 5 GENERAL ARTICLES ... 5CII COST AND SCHEDULE TASK FORCE REPORTS ... 9QUANTIFICATION OF THE EFFECTS OF CHANGES ... 9IMPACT ON COST AND SCHEDULE GROWTH ... 10CATEGORIES OF CHANGES ... 14SYNOPSIS ... 21
3. CONCEPTUAL IMPACT ON LABOR PRODUCTIVITY ... 23 BACKGROUND ... 23FACTOR MODEL ... 23ORDER OF MAGNITUDE OF IMPACTS ... 25HOW CHANGES AFFECT LABOR PRODUCTIVITY ... 26
4. DATA COLLECTION AND PROCESSING ... 31 OVERALL ASPECTS OF THE STUDY ... 31DATA COLLECTION PHASE ... 31
Definition of Study Parameters ... 33Project Selection ... 33Data Collection Procedures ... 35
DATA PROCESSING PHASE ... 36Apply Conversion Factors ... 36Calculate Equivalent Quantities ... 38Screen Data Set ... 40Determine Baseline Productivity ... 40Calculate Performance Ratios ... 41
5. ANALYSIS OF CHANGES ... 43 RELATIONSHIPS BETWEEN PERFORMANCE AND OTHER FACTORS ... 43SIGNIFICANCE OF INDIVIDUAL FACTORS ... 44MULTIVARIANT ANALYSIS ... 47
6. ANALYSIS OF DISRUPTIONS AND REWORK ... 49 INFLUENCE ON EFFICIENCY ... 49
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FREQUENCY OF OCCURRENCE ... . 49DISRUPTION FREQUENCY BY TYPE ... .. 52QUANTITATIVE EFFECT OF DISRUPTIONS ... .. 52
7. PREDICTIVE MODEL ... ... 57 SCOPE AND LIMITATIONS ... ... 57ILLUSTRATIVE EXAMPLE ... . 57CHANGE SITUATIONS ... . 57SYNOPSIS ... . 60
8. SUMMARY AND CONCLUSIONS ... .. 61 SUMMARY ... .. 61
Literature Review ... .. 61Conceptual Model ... ... 62Data Collection and Analysis ... ... 62
CONCLUSIONS ... . 63
REFERENCES ... . 65 APPENDIX: CII CHANGE ORDER IMPACTS ... ... 67
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LIST OF FIGURES
Figure Page
1. Cause effect matrix ... 72. Change price influence diagram ... 83. Electrical and mechanical work ... . 114. Civil and architectural work ... ... 115. Money left on the table ... .. 126. Number of bidders ... .. 137. Conceptual representation of the Factor Model ... . 248. Order of magnitude impacts to labor productivity caused
by disruptions ... ... 279. The Factor Model ... ... 28
10. Data collection, processing, and analysis process ... ... 3211. Efficiency as a function of various factors taken
one at a time ... . 4612. Influence of rework and disruptions on efficiency ... ... 5013. Increase in the percentage of days as a function
of rework and disruptions ... . 5114. Change in percentage of disrupted days by type ... . 5315. Conceptual model for estimating labor efficiency ... .. 59
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LIST OF TABLES
Table Page
1. Matrix of types and origins of changes ... .. 152. Change orders and criteria for classification ... . 163. Category of change listed by cause ... ... 174. Type and definition of change order ... .. 185. Range of impacts resulting from changes ... . 306. Project features ... ... 347. Example listing of bulk commodity items ... . 388. Daily quantities installed for the example crew ... .. 399. Data used in calculating baseline productivity ... ... 41
10. Correlation matrix ... . 4411. Significance of various factors on the performance ratio ..... 4512. Summary statistics of regression models ... . 4813. Influence of rework, disruptions, and changes on efficiency .. 4914. Percentage of days ... . 5215. Percentage of disrupted days by type ... . 5416. Quantitative effect of disruptions ... ... 55
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EXECUTIVE SUMMARY
Changes and change orders are a source of concern to owners and contractors alike. Changes are an important contributor to cost overruns, schedule extensions, and contract acceleration. This report examines the quantitative effect of changes on labor productivity using productivity data from four active construction sites. In total, the database contains 151 weeks of data and shows a significant average loss of efficiency when performing changes work. Importantly, the report also investigates the reasons why there is a degradation of labor productivity. Understanding the causes is a prerequisite to formulating effective strategies for managing changes.
The report begins with a brief literature review on changes and change orders. It shows that there is little substantive information on the quantitative aspects of changes and that most articles seem directed more toward publicity and advertising. The activities of the Change Impact Task Force to categorize changes is also reviewed.
The analysis of the productivity data reveals that, on average, the efficiency when performing changes work is about 70 percent of normal, that is, there is a 30 percent loss of efficiency. Various statistical analyses are explained that validate this conclusion. The research also reveals that it is possible to perform changes work without loss of efficiency. About a third of the days when changes work was done show no loss of efficiency.
Additional analyses support the conclusion that changes negatively affect labor efficiency. First, it is shown that labor efficiency is negatively affected by disruptions to the work. Next, it is shown that when changes work is performed, there are more disruptions. The percentage of days when disruptions occur increase by about 50 percent when changes occur compared to normal days. The conclusion of this analysis is that disruptions are the proximate cause of loss of efficiency.
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The reasons why there is loss of efficiency are investigated by examining the disruption by type. The data reveals that when changes work is performed, there is an increase in disruptions caused by the lack of availability of materials, tools, and equipment. The most significant increase, a four-fold increase, occurs in material deficiencies. There is also an increase in performing out-of-sequence work.
Based on this work, it is concluded that the principal factor leading to disruptions and loss of efficiency is timing. Late changes are particularly detrimental. If changes are identified early, before the work is given to the crafts, the effect of changes is probably minimal. However, if the crew, through their work, identifies the need for a change, then there is an adverse effect of having to stop and wait for information and materials. If the extent of the change cannot be resolved at that point in time, further negative effects will occur. The crew must move to another work location, one for which they may not have planned the work, and returning to the location of the change at some later time may require support services or outage permits. Thus, the early identification of the need for a change is an important element in cost-effective construction.
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CHAPTER 1 INTRODUCTION
Changes during construction are a particularly irritating and costly problem for the contractor. It is recognized that some changes are necessary and inevitable. However, many argue that changes are time consuming and expensive compared to the cost of the original scope of work. Yet there is a serious lack of understanding about the impact of changes. To date, there have been no definitive studies reporting in quantitative terms the impact of changes.
OBJECTIVES
The objectives of this report are to:
o Quantify the impact of changes on field labor efficiency o Determine the relationship between changes and various types of
disruptions o Establish why and to what extent changes affect labor efficiency o Present a conceptual model for estimating the impact of a particular
change on labor efficiency
SCOPE
The cost of changes is comprised of largely indirect, engineering, material, equipment, and labor costs. Of these, labor costs are the most difficult to estimate, control, and measure. Since workhours are directly related to labor costs, the impact of changes is measured by quantifying workhour inefficiencies at the crew level.
DEFINITIONS
In this report, the following definitions are used:
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Changes--A reference to any change made to the original scope of work. No distinction is made regarding whether payment is promised or why the change was needed. There is no association in this report with construction disputes or claims.
Conversion Factors (CF)--The ratio of unit rates based on the earned value concept used to convert quantities of one or more items to an equivalent quantity of another single item.
Disruption--An event that is known or has been reported in the literature to adversely affect labor productivity. Examples include lack of materials, lack of tools or equipment, congestion, and accidents.
Disruption Factor (DF)--The ratio of the average daily number of non weather related disruptions during a baseline period divided by the average number of disruptions for a particular week. A number less than unity means more than the average number of disruptions.
Disruption Frequency--The average number of disruptions per 10-hour workday.
Disruption Index (DI)--The ratio of the performance factor on days when a certain type of disruption occurred divided by the average performance factor on days when no disruption occurred. The index shows the level to which output was reduced because of the particular type of disruption.
Earned Value--This technique is used for calculating the percent complete of a control account. It uses a weighted average approach in which the weight assigned to individual accounts in the control account is based on the initial workhour estimate for the account.
Efficiency--The relative loss of productivity compared to some baseline period. A value less than unity means a performance poorer than normal.
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Performance Factor (PF)--The ratio of the expected or baseline unit rate divided by the actual unit rate. It is defined such that values greater than unity mean that performance is better than expected.
Performance Ratio (PR)--The inverse of the performance factor.
Productivity--This parameter is defined as the workhours during a specified time frame divided by the quantities installed during the same time frame. The time frame can be daily, weekly, or the entire project (cumulative). It is commonly called the unit rate.
Rework--A subset of changed work. It usually involves correction or removal of earlier work. The cause can be design or construction related and can be caused by poor workmanship, design errors, design changes, misreading of drawings, weather, fabrication errors, and so forth.
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CHAPTER 2 BACKGROUND
There have been numerous articles written about changes and change orders. These are divided into the following categories: general articles, Construction Industry Institute (CII) Cost and Schedule Task Force reports, articles quantifying the effects of changes, a CII Changes Impact Task Force investigation of the influence on cost and schedule growth, and the CII Changes Impact Task Force investigation of the categories of changes and factors. These are described below.
GENERAL ARTICLES
Many articles have been written describing in general terms the effects of changes on labor productivity. Most deal with legal aspects, documentation, substantiation, and claims management. Many of the articles appear to have been written for promoting business interest. An article by Dellon and Dellon (1988) is typical. The basic causes of claims as listed by the authors are:
o Schedule variations o Field conflicts o Access delays o Change orders o Acceleration o Work out of sequence o Owner furnished equipment delay o Owner furnished material delay
Dellon goes on to outline the owners and contractors position for damages by citing a laundry list of potential impacts. Steps of an action plan in the event of a claim and guidelines for support system for documentation are also included. The article concludes by
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presenting a matrix that relates potential impacts to the eight basic causes. From looking at the matrix in figure 1, all eight causes (effects) can lead to each of the impact types. Therefore, the article is too general to be of much value in preventing claims. Its main contribution is the list of documents needed to support a claim.
Several articles describe resolution techniques (Yanoviak 1987). Most provide the author's perspective on negotiated settlements, arbitration, and litigation. The emphasis is on after-the-fact documentation and resolution techniques. Little guidance is provided for understanding or minimizing the impact of changes.
Substantiating cost is the subject of a paper by Dieterle and Maziarz (1991). A distinction is made between incurred cost and claimed cost. The author argues that much of the difference results from federal and state unemployment taxes, social security payments, and other elements of payroll burden.
The author also compares the Blue Book and CRG equipment rental rates, and shows that the monthly charges for the a D7G dozer and an Insley 600 excavator are considerably less when the CRG Ownership rate is used. The author then details parts of the Federal Acquisition Regulation (FAR). The focus of the paper seems to be on accounting procedures.
A University of Texas thesis sponsored by the Construction Education Institute provides a good general description of the influence of changes on cost (Brown 1988). A forward-change pricing concept is presented, and an influence diagram is developed. However, as is typical of most reports and articles, the diagram shows everything affecting everything else. The diagram is shown in figure 2. The concept of the ripple effect is also discussed.
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CII COST AND SCHEDULE TASK FORCE REPORTS
The CII Cost and Schedule Task Force compiled a literature review of articles and reports describing changes and the development of management techniques related to changes (Hester et al. 1988). It contains a comprehensive bibliography.
The Task Force also prepared a report on the impact of changes on cost and schedule (Zietoun and Oberlender 1993). They concluded that changes were inevitable on all projects, but were most troublesome on fixed price or guaranteed maximum price contracts--therefore, the consequences need to be understood by the person directing the change. Recommendations for managing the changes process to minimize impacts were presented. This is one of the few sources of information directed toward prevention and minimization of the effects of changes.
QUANTIFICATION OF THE EFFECTS OF CHANGES
Very little information has been published on the quantification of the effects of changes. The two articles described below rely on data from contract claims and disputes.
Zink (1990) described a measured mile method (MMM), which is a method for establishing a baseline for quantification purposes. The focus of the paper is on labor efficiency. The author suggests that the MMM can be used to quantify workhour overruns of an unimpacted trend as a percentage of work completed in each labor division. The methodology relies on a workhour trend curve rather than a productivity trend curve. An example is given on how to assign responsibility for each significant event to a particular party.
Leonard (1987) described his quantification investigation of the effects of changes on labor productivity. The study was based on the detailed review of 90 claim cases, each from
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a separate contract. The cases were divided into two groups: civil/architectural and mechanical/electrical. The major causes of labor overruns were:
o Loss of rhythm o Schedule deterioration o Unbalanced crews o Acceleration o Increased site administration o Manpower fluctuation
Figures 3 and 4 show the percentage loss of productivity as a function of the percentage of change orders. The slope of the curves is much shallower than might be expected. A four- or five-fold increase in the percentage of changes leads to a 10 to 20 percent loss of productivity. Leonard also reported that as the hours spent on changes exceeded about 10 percent of the total hours, there was a noticeable departure in the curves of earned and actual hours. Although not stated, this may be caused by the ripple effect.
IMPACT ON COST AND SCHEDULE GROWTH
The CII Changes Impact Task Force investigated 106 projects to determine the factors impacting cost and schedule growth (Zietoun and Oberlender 1993). The data were sorted according to fixed-price and costreimbursable contracts. A number of factors were examined. Figures 5 and 6 show the relationship of money left on the table (MLOT) and the number of bidders to cost and schedule growth on fixed-price projects. On costreimbursable projects, three driving factors were studied: cost, schedule, and quality. Cost overruns were more acute when the primary driving factor was schedule. Schedule overruns were more frequent when the driving factor of cost was most dominant. The quality driving factor was not a major factor in either type of overrun.
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CATEGORIES OF CHANGES
The Changes Impact Task Force recognized that there is much misinformation about individual changes. Many changes are necessary and some are not costly in terms of labor hours. The task force began by developing a matrix of types of changes and their usual origin. This matrix is shown in table 1. As can be seen, changes can originate from many sources.
The task force also examined how changes might be classified. A conceptual list included the following:
o Changes to process design o Changes to plot plan and/or equipment arrangements o Changes to the specification of equipment items o Changes to the specification of bulk items such as
instruments o Operations-directed changes for operability and
maintainability o Value-engineering changes
The task force felt that changes classified in this manner would be too general and would not aid in understanding of control.
Other possible classifications were presented. These lists includedcriteria for classification and expected impacts. (See tables 2, 3, and4.)
There is considerable similarity among the lists, reflecting commonality among practices. However, these classifications are reflective of the need for backcharging for changes and were likely developed for accounting purposes.
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Table 3. Category of change listed by cause.
1. Improper scope definition
2. Changing owner needs and requirements
3. Lack of timely involvement by owner throughout the life of the project
4. Fast-tracking
5. Changes in staffþboth owner and design firm
6. Errors
7. Omissions
8. Deficiencies in estimating
9. Value engineering and attempts to reduce costs
10. Lack of sufficient detail in design documents "Conceptual Design"
11. Unforeseen requirements by regulatory agencies (building inspectors, FDA, etc.)
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One interesting classification scheme characterized changes in terms of the following three categories:
Categories of Changes
1. Characterized by contract clause:
o Time extension (for excusable delays) o Acceleration (early completion) o Changes (amplification below) o Termination/suspension of the contract/work o Differing site conditions o Owner furnished materials/equipment o Value engineering changes
2. Characterized by cause:
o Design error o Differing site condition o Adverse weather (time extensions, usually) o Owner directed changes (simple change of mind) o Newly available technology/products/processes o Field Changes (usually minor--to facilitate construction) o Improved operability o Value engineering
3. Characterized by what is changed:
o Design of a system/process o Technical specifications for materials/systems/processes o Layout/configuration of the project o Size of the project--increase/decrease o Price(s) to be paid the contractor by the owner o Overruns/underruns of estimated quantities o Time allowed for completion o Sequence/Order of work o Project management/administrative items (i.e. safety, submittal
processes, as built drawings, operation manuals, extra testing, etc.)
As can be seen, the lists are quite general and do little at clarifying the impact aspects. Each task force member could cite reasons why a common classification would not aid in the process of understanding changes.
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The task force next turned its attention to possible impacts from changes. Primary and secondary impacts were identified and are listed in the following index of changes:
INDEX OF CHANGES
Expected Primary Impact Expected Secondary Impact
o Increased construction costs o Increased cost and schedule -Labor -Supervision delays due to:-Material A. Decreased worker productivity-Equipment B. Lowered design team morale
and productivityo Schedule delays C. Relocation of labor
D. Increased planning,o Demolition or dismantlement coordination, and
of previously installed work rescheduling activities E. Possible out-of-sequence work
o Increased design costs F. Demobilizing, remobilizingG. Acceleration
1. Overtime (fatigue) 2. Crowding
H. Possible seasonal/weather related impacts due to delays
I. Increased effort to price out and negotiate the changes
J. Learning curve associated with a change
o Changes not fully coordinated lead to more changes
o Increased cost leads to further value engineering
o Possible litigation
The task force agreed that many of these impacts were difficult to track.
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As an aid to managing changes, the task force prepared a checklist of the most common parameters to review when considering a change. The major categories are:
o Size and scope o Nature of the change o Timing o Management impact o Who does the change o Site conditions (environment)
The complete listing of guidelines is included in appendix A.
SYNOPSIS
There is little substantive information available on construction changes. The most reliable and neutral information was prepared by CII.
The general articles identified provide information about how to quantify a change after impacts have occurred and how to manage the changes process from the claims viewpoint. Most articles seem to be directed more to publicity and advertising than to avoiding claims or adding to the body of knowledge on avoiding or understanding changes.
Only two articles were found that described efforts at quantifying the impacts of changes. Both articles relied on data from claims. The analysis techniques were at times suspect.
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CHAPTER 3 CONCEPTUAL IMPACT ON LABOR PRODUCTIVITY
The construction environment is greatly misunderstood by many professionals. This chapter explains a model that aids in the understanding of the construction environment.
BACKGROUND
Prior research has identified that in ordinary situations there are two major factors affecting site labor productivity: organizational continuity and executional continuity (United Nations 1965). Organizational continuity relates to the work that needs to be done and encompasses the physical components of the work, specification requirements, design details, and so forth. Executional continuity relates to the work environment and how well the job is organized and managed. Management aspects covered in this category include weather, material and equipment availability, congestion, out-of-sequence work, etc.
Others have tried to develop models of labor productivity. Unfortunately, these models are based on erroneous concepts borrowed from industrial engineering (Thomas et al. 1990) or are too complicated to be useful (Brown 1988). None of these models have been validated with data.
The conclusions of the United Nations' report have been validated by research done at The Pennsylvania State University (Thomas et al. 1989). This research has led to the development of the Factor Model.
FACTOR MODEL
The Factor Model is so named because it is based on the factors that affect labor productivity. A conceptual representation is shown in figure 7. The flow of inputs and outputs is comparable to a pipeline. The work to be done and the work environment are
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analogous to catalysts in the form of information, resources, and conditions needed to efficiently convert workhours into output. Where these are deficient, as would be the case if materials or equipment were not available, inputs cannot be efficiently converted to outputs.
Another element of the Factor Model that is not shown is the cumulative or ripple effect. This occurs when the project conditions have deteriorated to the point where work on an activity is adversely affected by another activity or by the mere nature of the site environment. No research has been conducted on the ripple effect, although it has been acknowledged quite often by construction professionals. Furthermore, defining when the ripple effect applies is often limited to a post project analysis. One suggestion is that it occurs when the number of workhours devoted to changed work or rework exceeds 10 percent of the workhours devoted to the scope of work (Leonard 1987).
ORDER OF MAGNITUDE OF IMPACTS
Current practice is reasonably well developed to estimate the added labor hours for the work to be done. Contractor data bases based on historical information can generally isolate the differences in pipe sizes, design details, or changes in specification or quality requirements. Perhaps the most uncertainty arises when the work scope is affected, as would be the case when a change is added and the quantity of new work is not very large. Here, the startup and mobilization effort cannot be spread over a large scope of work. Past research (Sanders and Thomas 1991) has shown that unit productivity can vary by as much as 3:1 because of scope reductions. This fact is often obscured, largely because contractors are accustomed to reviewing cumulative productivity numbers or weekly summaries instead of unit data. Overall, Sanders showed that changes in the work to be done or work content can cause labor productivity to vary by more than 2:1.
Estimating practice can also handle changes in the work environment, although the basis of these estimates is often empirical. All large contractors have rules of thumb about inefficiencies when the work is to be done in winter, the work location is above ground, the work area is congested, or when the facility is in operation. Ongoing research has shown
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that the work environment can cause variations in unit data by as much as 4:1. The magnitude depends on many factors, such as the nature and severity of the condition. There are no firm guidelines for estimating the "markup" for less than satisfactory working conditions.
While much has been said about the ripple effect, there have been no quantitative studies showing the magnitude of these effects. Projects where the ripple effect occurs are typically involved in litigation, so data are usually not available. However, studies at The Pennsylvania State University have indicated that their impacts can be as great as 7:1 or more. This figure seems quite large, but one must remember that these are very unsatisfactory projects. But, for projects not involved in claims, ratios as high as 4:1 have been documented.
These conclusions are summarized in figure 8. The figure is not presented as a tool for estimating losses of efficiency, but as an indicator that these orders of magnitude of efficiency losses can occur.
HOW CHANGES AFFECT LABOR PRODUCTIVITY
A detailed representation of the Factor Model is shown in figure 9. The work environment portion shows 10 variables that can be influential. While there can be many others, these 10 are the ones that are most common.
The presence of changes is an indirect factor and can be part of the work environment or, in extreme cases involving many changes, can cause ripple effects. The indirect concept of changes is because of the view that changes themselves do not lead to productivity losses. If it did, the losses would be automatic, which most professionals agree is not the case. Instead, a construction change may cause other variables to be activated. Consider the following situation where an owner realizes that an important pressure sensor has been inadvertently left off of the plans. This problem is recognized after much of the work is finished. New conduit and cable are required.
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The small sensor has thus led to a larger scope operation, which is the installation of new conduit. But the impact on the workhours required can be much greater than the installation of conduit. The conduit installation can be much more difficult because of several important factors. The work area is probably much more congested than it would have been had the conduit been part of the original scope of work. Additionally, parts of the facility may be operational, necessitating permits and other clearances.
Another factor that can seriously affect the work is that the work is done out of sequence. The crew doing the work needs to stop their present assignment and plan and reorganize for the new work. This phenomenon is sometimes called loss of momentum or loss of rhythm. The crew may have to arrange for scaffolding, revise the routing schedule, coordinate with other crews, and plan many other elements of the work in a level of detail that would not have been required had the sensor been included in the original design.
Another often overlooked factor is the scope of work involved. The crew may have been routinely installing 1,500 ft of conduit per day on production type work. Now, they are being asked to plan and install 150 ft. The time measured on a per foot basis may be 2 to 4 times greater. Because the extended initial planning and setup period is distributed over a much smaller scope of work, the result is likely to be reflected in more workhours per ft. Where the learning curve effect is present, a small scope of work is always done on the learning end of the curve. The labor component may be several times greater than ordinarily required.
It is little surprise that work bid at one rate may take 2 to 4 times more workhours when done as changed work. This scenario leads to an important assumption: loss of productivity on changed work is the result of changes in the scope and complexity of the work and changes to the environment in which the work is done. The latter is probably most severe.
Pricing methodologies for changed work are often weak. Owners frequently think that contractors make money on changes because their estimates are too high. In reality,
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contractors often lose money on changes because their estimates are too low. Table 5 lists the major factors affecting changes and the average range of workhour increases that can be expected. Not all the factors listed will apply to a single change.
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Obviously, the magnitude of workhour increases can range from zero to very significant. If there are a large number of changes, the multiplier would have to be increased because of the ripple effect.
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CHAPTER 4 DATA COLLECTION AND PROCESSING
Chapter 4 details how the data were collected and processed. This process is shown in figure 10. Each of these steps is described below. Many of the deficiencies in earlier studies are addressed by the data collection and processing procedures. Using these procedures, it is possible to gather consistent data from many projects and combine these into a single data base for analysis.
OVERALL ASPECTS OF THE STUDY
This study has several aspects that are different from most previous studies. These are summarized below.
The smallest manpower unit that produces completed output is the crew. Therefore, the focus of this study is on the output of an average crew. The study includes crews of electricians and/or pipefitters from four active construction projects.
Most previous studies have relied on cumulative productivity data. In this study, unit data (daily) are the principle data format.
DATA COLLECTION PHASE
The objective of the data collection process is to assure that consistent data from many projects can be collected and then combined into a single data base. This is accomplished by defining the study parameters, carefully selecting projects, and applying uniform data collection procedures.
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Definition of Study Parameters
The analysis process is much easier if the data are homogenous. In this report, only the electrical and piping crafts are studied. The rationale is that these crafts represent the majority of the work, and they are the crafts most likely to be affected by changes. The work performed by these crafts was further narrowed to crews performing production-related work. For electricians, production-related work involves the installation of conduit, cable and wire, terminations and splices, and junction boxes. For piping, this work includes pipe erection and the installation of supports and valves. Crews performing other kinds of work were not considered for study.
Project selection is also an important consideration for removing influences that could potentially make the data difficult to analyze and interpret. The labor environment should be tranquil and there should not be an inordinate number of changes. Experimental, unique, or poorly designed or managed projects should be avoided. In this study, each of these criteria were met. Further, the early phases and the startup phase should not be included in the study.
The study duration should be sufficient to include work on changes. In this study, a duration of 14 weeks was specified.
Project Selection
In this study, productivity data were collected from four active construction projects for the electrical and mechanical crafts as shown in table 6. There were a total of 151 weeks of data collected.
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The projects were constructed in the 1989-1992 time frame. Each was constructed in a tranquil labor environment and was well designed and managed. None experienced any unusual difficulties that would have caused it to be considered abnormal.
The manufacturing and paper mill projects were existing facilities where old systems and equipment were removed and new ones installed. Congestion was a concern in both facilities. The process plant was a spacious, outdoor, grass roots facility. The refinery project involved the rebuilding of an outdoor facility where demolition of portions of the existing facility was required. Congestion was a problem at this plant.
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Data Collection Procedures
The data collection effort was independent of the cost reporting system. A procedures manual was developed for this purpose (Thomas 1991). Site personnel collected the data. The philosophy and evolution of the procedures manual are explained elsewhere (Thomas et al. 1989). The data collection effort was organized around the completion of eight forms. Seven forms were completed daily; the other was completed once. The forms solicited information about the crew size, absenteeism, the quantities installed, and the conditions in which the work was done. Selected information requested on each form is as follows:
Form No. 1--Manpower/Labor Pool: Crew size; Crew composition (skilled and unskilled); Absenteeism
Form No. 2--Quantity Measurement: Measured units completed for each subtask
Form No. 3--Design Features/Work Content: Work type; Design details
Form No. 4--Environmental/Site Conditions: Temperature; Humidity; Weather events
Form No. 5--Management Practices: Delays; Material, equipment, and information availability; Congestion; Sequencing; Rework
Form No. 6--Construction Methods: Length of workday; Overtime schedule; Working foreman
Form No. 7--Project Organization: Size of project work force; Other site support personnel; Number of foremen
Form No. 8--Project Features: Type of project; Approximate cost; Approximate planned duration
The type of data recorded was continuous, integer, and binary. An example of continuous data is quantity of conduit, i.e., 74.6 ft. Integer data includes the crew size, i.e., nine tradesmen. Binary data are an important component of data collection. Binary variables take on values of 0 or 1, depending on whether a particular condition is present.
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For example, if a measurable portion of the workhours were spent on changed work, 1 would be recorded; if not, the factor would be recorded as 0.
As can be seen, the forms requested information about the two main categories of factors affecting the work identified in the U.N. report (United Nations 1965) and shown in figures 7 and 9. Form Nos. 1 and 2 cover the inputs and outputs. Form No. 3 covers the work to be done, and Form Nos. 4, 5, and 6 cover the work environment. While the data forms are more detailed than shown above, every effort was made to streamline the data collection process. Following an initial familiarization period, data collection typically took about 30 minutes per day.
DATA PROCESSING PHASE
The purpose of the data processing phase is to calculate the daily productivity had the crews been installing the same item, screen the data for unusual peculiarities, and normalize the data so that performance is related to a baseline productivity when there were no changes or disruptions to the work.
Apply Conversion Factors
It is a known fact that different size components require different labor resources to install. For example, 4-in conduit requires more workhours per foot to install than 3/4-in conduit. Differences such as these exist for all the items included in the study. These differences are accounted for by using conversion factors. The logic is as follows.
The first step is to define a standard item. In theory, the choice of an item is irrelevant. In practice, it is usually selected as the item that occurs most frequently. In this
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study, the standard item for electrical work was 2-in galvanized rigid steel conduit and for piping 2 1/2-in schedule 40, butt welded, carbon steel spools.
Next, un factored unit rates were obtained from standard estimating manuals. For electrical work, the Means and Richardson manuals and a manual from a construction company were consulted. For piping work the Means, Richardson, Page & Nations, and the manual from the same construction company were used. The use of multiple estimating manuals precludes the factors from being influenced by one source.
Using the data from a single estimating manual, conversion factors for each item are calculated as
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where i = the item number and j = the manual number. Once CF values have been calculated for all manuals and items, multiple regression techniques can be used to develop a mathematical relationship for each grouping of like items. Groupings are for conduit, cable, piping, valves, etc. The group regression equation can be used to calculate an estimate of the conversion factor for each item in the group.
In practice, the conversion factor shows how much more or less difficult an item is to install compared to the standard item (Sanders and Thomas 1991). The theory behind conversion factors is that of earned value. It can be easily verified that irrespective of the mix of quantities installed, the conversion factor does not alter the hours earned in a given time frame.
Conversion factors are analogous to monetary exchange rates. For example, a mix of marks, yen, and pounds can be exchanged for an equivalent amount (or value) of pounds or another currency such as dollars.
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The utility of the conversion factor approach is that the productivity of crews doing a variety of work can have their output expressed as an equivalent output of a single standard item. Thus, the productivity of all crews can be calculated for the same standard item during each time period regardless of the work performed. Likewise, crews from different projects can have their productivities calculated for the standard item, meaning that the data from multiple projects can be combined into a single data base because all the productivity values represent installing the same item of work.
To illustrate, calculate the conversion factors for the items listed in table 7. The standard item is 2-in GRS conduit. It is assumed that there is only one source of unit rate data. The conversion factors in the last column are calculated using Eq. 1.
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Calculate Equivalent Quantities The equivalent quantities are the number of square feet of the standard item that will yield the same number of earned hours as was actually earned by installing the non standard items. Practically speaking, it is the most likely estimate of the quantity of the standard item that would have been completed for a given set of site conditions. The equivalent quantity is calculated using Eq. 2.
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Suppose on a given day, a crew installs the quantities listed in the first two columns in table 8. The conversion factors in table 7 are used in Eq. 2 to calculate the equivalent quantities. As shown in table 8, the crew did the equivalent of 200 ft of 2-in GRS conduit.
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The workhours earned are determined by multiplying the quantities installed by the unit rate from table 7 (Thomas and Kramer 1987). For the actual installed quantities in column 2, table 8, the crew earned 35.6 workhours. If the earned hours are calculated based on the equivalent quantities in column 4, table 8, the unit rate for the standard item (2-in GRS conduit) from table 7 is used. This value is 0.178 wh/ft. The earned workhours is also equal to 35.6. Therefore, the value of the work in terms of earned hours is the same, it is simply expressed in a different way. If a different standard item were chosen, the earned workhours would still be 35.6.
Screen Data Set
The data were examined for abnormalities that could confuse the analysis process. These data were removed from the data set. Included as abnormalities were days when no workhours were recorded. There were several days when unusually large quantities were installed. It was rationalized that these data included quantities that were installed during previous periods, and they too were deleted. On some days there were unusually large numbers of workhours recorded. These could not be explained. All days with more than 300 workhours were deleted. On projects 9183 and 9184, there were no changes recorded. Data from these two projects were deleted. Furthermore, the latter portion of the data on project 9185 were incomplete. These data were also deleted. The data base was reduced in the screening process from 637 to 372 data values.
Determine Baseline Productivity
The baseline productivity was calculated for each project by determining the workhours and quantities installed on normal days. Normal days were those where there were no changes or rework, disruptions, or bad weather reported. Table 9 shows the summary data used to calculate the baseline productivity. The following equation was used:
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Workhours Productivity = ------------- (3)
Units of Work
It is recognized that productivity calculated using Eq. 3 is commonly called the unit rate.
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Calculate Performance Ratios
The emphasis in this study is on the effect of changes on labor productivity. The departure from normality is calculated using Eq. 4.
Actual Productivity Performance Ratio (PR) = --------------------- (4)
Baseline Productivity
Performance ratios were calculated for each of the 372 data values in the data base. A PR value greater than unity means that based on the daily quantities, more workhours were required that day than on the average baseline day, that is, the productivity was worse.
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CHAPTER 5 ANALYSIS OF CHANGES
The previous chapter explained how the data were collected and processed. Following these steps, the data base contained 372 data values or records, one for each workday. For each workday, there is a calculated performance ratio showing the actual workhour resources compared to the baseline or normal resource requirements. This chapter examines how these resources are influenced by performing changes work.
RELATIONSHIPS BETWEEN PERFORMANCE AND OTHER FACTORS
The initial investigation was a correlation analysis to identify the relationship between various factors. The factors selected for this analysis and the correlations are shown in table 10. The first number in table 10 is the correlation coefficient, followed by the number of data points. The last number is the level of significance, which ranges between 0.000 And 1.000. A value near 0.000 means a highly significant relationship.
As can be seen from the correlation matrix, changes work is highly correlated to the performance ratio, rework, and disruptions. Rework is also highly correlated to disruptions. Weather events are highly correlated to the length of the workday, and work on pipe supports is significantly correlated to the performance ratio. The arithmetic signs of the correlation coefficients are as intuitively expected. A positive sign means that the occurrence of the factor is associated with worsening performance.
Only 252 data points are used in this analysis because that was the number of records for which a value was recorded for all factors. The factor limiting the number of data points is work on pipe supports, which was not documented in the earlier studies. Therefore, it is possible that when examining the influence of multiple factors, the results may differ
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somewhat from those shown in table 10. Nevertheless, the results of the correlation analysis are reasonable and logical.
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SIGNIFICANCE OF INDIVIDUAL FACTORS
Of particular interest is the influence on the performance ratio of changes, rework, disruptions, and weather. For this investigation, an analysis of variance (ANOVA) test was done for each factor. The length of workday is not analyzed because the cause of shortened workdays was attributable to bad weather, and is, therefore, already considered.
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The results of the analysis are shown in table 11. The principle statistic from an ANOVA test is the level of significance. A value for the level of significance near 0.000 indicates a highly significant relationship. Another by-product of the ANOVA analysis is the average performance ratio for the conditions being examined, for example, changes work or no changes work. These averages can be used to compute an average efficiency for that condition. Efficiency is calculated using the following equation:
Avg. Performance Under Normal Conditions Avg. Efficiency = ---------------------------------------- (5)
Avg. Performance with the Factor Present
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ratio.
As is evident, when only one factor is considered at a time, there is a degradation in performance when the first four factors are present. These efficiencies are shown graphically in figure 11. Pipe-support work shows a significant positive contribution to performance. This peculiarity may be caused by the way supports were credited by the cost reporting system.
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MULTIVARIANT ANALYSIS
There is always some uncertainty in performing single variable analyses since the influence being measured, in this case performance ratio, may be influenced by some other factor. For this reason, a multivariant regression model was developed. Each of the variables in table 11 is a candidate for the model. The length of the workday was also considered. However, since changes, rework, and disruptions are highly correlated (see table 10), only one of these three factors can be included in the model at any one time.
Five multivariant-regression models were developed using the changes, weather, pipe support, and length of workday factors. The choice of the best model was based on the standard error, and the mean absolute error. The variables used in each model and relevant statistics are shown in table 12.
There are minor differences between the models, and, therefore, the simplest model (model 3 in table 12) is selected and is shown as Eq. 6.
Performance Ratio = 2.57 + 1.07 x Changes (6)
The relative values instead of the absolute values are important. Focusing on the changes work only, the average performance ratio when no changes work is being performed (the binary changes variable has a value of 0) is 2.57. When changes work is being performed (as denoted by the value of 1 for the binary changes variable), the average performance ratio increases to 2.57 + 1.07 = 3.64. Using Eq. 5, the average efficiency is 0.71, or a loss of efficiency of about 30 percent. The efficiencies for the various models as shown in table 12 were between 0.66 and 0.79. Four of the models showed efficiencies between 0.66 and 0.71. In general, the average losses of efficiency are in the range of 30 to 35 percent.
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CHAPTER 6 ANALYSIS OF DISRUPTIONS AND REWORK
Table 10 in the previous chapter showed that the occurrence of changes was highly correlated to rework and disruptions. This chapter further examines this relationship.
INFLUENCE ON EFFICIENCY
The influence of rework and disruptions on changes and efficiency was investigated. For this analysis, the data set was partitioned into two parts, normal and changes work. Each subset was further partitioned according to the presence of rework and disruptions as per table 13. The average efficiency was calculated using Eq. 5. The results are shown in table 13 and figure 12. The number of data values in each cell is shown in parentheses.
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efficiency.
What is obvious is that changes work increases the losses of efficiency. Within every column there is a significant degradation in efficiency when performing changes compared to not doing changes work.
FREQUENCY OF OCCURRENCE
Table 14 and figure 13 show the increase in the percentage of days where there is rework or disruptions when changes work is considered. For example, when there are no
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changes, rework was done on 26 percent of the days and some disruption occurred on 58 percent of the days. When changes work is considered, the percentages increase to 64 percent and 88 percent, respectively. It is clear that changes is linked to more rework and disruptions.
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DISRUPTION FREQUENCY BY TYPE
The frequency of occurrence of disruptions by type expressed as a percentage was also investigated. The results of this analysis are shown in table 15 and in figure 14. The most obvious disruption type is material availability or management where there is a very significant increase in the percentage of days when there are changes. While the percentages of the other disruption types are within the range of error for the method of measurement, there are increases in all categories except the congestion disruption.
QUANTITATIVE EFFECT OF DISRUPTIONS
The quantitative impact of disruptions on labor efficiency is examined by developing a multiple regression equation where the performance factor is the dependent variable and the disruption types are the independent variables. Disruption indices were then calculated that were the ratio of the performance when that disruption calculated compared to the performance when no disruption occurred. The disruption index shows the degree to which the crew output is reduced because of the disruption. Table 16 shows the disruption indices.
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It shows that when materials are not available, the crew output is 74 percent of normal. Lack of materials reduces performance by 26 percent. As can be seen, out-of-sequence work and lack of materials and information are particularly acute. Congestion and lack of tools and equipment showed minimal or no effect.
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Based on the results in tables 13, 14, 15, and 16, there is little doubt that disruptions cause a loss of efficiency and that as changes occur, there is a greater frequency of disruptions. This observation is consistent with the Factor Model and shows that it is a reliable model of labor productivity. Changes themselves do not cause losses of efficiency. To manage changes, one needs to manage the disruptive influences. Of course, the disruptive effects cannot be avoided in many instances.
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CHAPTER 7 PREDICTIVE MODEL
The data on this project were not in sufficient detail to develop a detailed predictive model of the impact of changes on labor productivity. Nevertheless, the information in chapter 7 can be used to construct a rudimentary predictive model.
SCOPE AND LIMITATIONS
The model described below is based on the timing of the change only. Scope and the work environment are not included. The model is conceptual and incomplete. The model has not been tested.
ILLUSTRATIVE EXAMPLE
Suppose a change is issued to install a branch conduit from a motor control center to a new device. The scope of work is small and the work is planned to take 3 working days. The following section shows how to calculate the labor efficiency for various change situations.
CHANGE SITUATIONS
With respect to the timing of a change, there are three situations that can arise. Each affects the labor utilization in a different way. These cases are described in the following example change:
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Case 1: Need for Change Identified in Advance
If the change is identified well in advance, the crew can perform the work as if it were part of the original scope. The material requisitions and preplanning can be done for the changes work at the same time as for the other work. There should be no impact, and the impact factor is 1.0.
Case 2: Need for Change Identified by Crew, Work Done at That Time
In this case, the need for the change is identified by the crew as other work is being performed. There is a disruption to the work as the crew seeks information and procures the necessary materials. The changes work is completed before support facilities like scaffolding are removed.
Figure 15 shows schematically that the first day is consumed by soliciting information. The crew efficiency for that day is 0.53 (see table 16). The next day when the work begins, the work is accomplished at a reduced efficiency of 0.74 because of difficulties in procuring materials. The last 2 days, the work is done at the normal rate of 1.0. Thus the efficiency for the change is the average of the four efficiency values (0.53 + 0.74 +1.00 + 1.00). This value is 0.81. Thus, the change would require about 23 percent more workhours than had the work been included as part of the original scope.
Case 3: Need for Change Identified by Crew, Work Done at a Later Time
This scenario is similar to case 2 except that the work cannot be performed at the time the need is identified. Performing the work later means that the crew will need to remobilize to do the work. The status of the job or work environment may be very different at that time. For instance, operating permits may be required or the work location may be more congested. These latter aspects are not considered as they are unique to the change.
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As shown in figure 15, the impacts of the change are now felt at two times. Initially, the need is identified, and for that time frame, information needs are identified. The reduced efficiency of 0.53 lasts for 1 day. At some later time, the work begins, but the impact of out-ofsequence work is present. This impact is largely a remobilization impact but also can reflect reduced time to plan the work. This impact probably lasts for several days. Material procurement and handling may also be a problem. The total impact is the average of the individual impacts (0.53 + 0.71 + 0.73 + 0.71 + 1.00) and is calculated to be 0.74. Thus, 35 percent more workhours would be needed to complete the same scope of work.
SYNOPSIS
The above analysis shows the impact of making changes to the work late in the job. The workhour requirements could be even higher if the work location has become more congested, the plant is in an operating status, or the work must be done in unfavorable weather. To minimize the impacts, managers and foremen need to plan the work carefully to avoid sending craftsmen to do the work without adequate resources.
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CHAPTER 8 SUMMARY AND CONCLUSIONS
This report summarizes an investigation into the quantative aspects of construction changes and rework. The focus is on labor productivity. The data were collected specifically for this project and do not involve construction claims.
SUMMARY
Literature Review
The published literature was examined, and it was found to be sparse with respect to the quantative aspects of changes. Many general articles were found describing how to document the impacts of changes. A number of articles detail how to track changes to enhance the opportunities for recovering dollar losses. Most articles of this type appear to be intended to promote business interest.
Of the articles and reports describing how to avoid changes and their negative impacts, most have been prepared by the Construction Industry Institute. The CII Cost and Schedule Task Force prepares several reports on the impacts of changes on cost and schedule. Recommendations for managing the changes process to minimize impacts were presented. The Changes Impact Task Force performed an analytical analysis of cost and schedule growth. A number of factors were examined, and conclusions were made relative to the type of contract, the type of project, the amount of money left on the table, the number of bidders, and several other factors.
Two articles were found that detailed the quantative effects of changes. One focused on labor inefficiencies and described a method of quantification based on workhours. The paper is conceptual, and relies on the development of a workhour trend curve. An example was presented on how to assign responsibility for each significant event to a particular party.
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The second article described the impact of changes on labor overruns as a function of the percentage of total hours spent on changes work. Impact curves were presented for civil/architectural and mechanical/electrical work.
Conceptual Model
A conceptual model was described that explained how changes and rework affected labor productivity. The model is based on disruptions. The model states that changes themselves do not cause losses of productivity, but instead cause disruptions to occur. It is these disruptions that cause productivity losses. Changes that can be made that avoid disruptions can be completed with little or no impact on labor.
Data Collection and Analysis
Productivity and related data were collected from eleven crews on five active construction projects. Data were collected daily. The data were screened and resulted in 372 data values. The activities studied were electrical and mechanical tasks. Equivalent footage of 2-in galvanized rigid steel conduit and 2 1/2-in schedule 40, butt welded, carbon steel pipe spools were computed. Baseline productivity values were computed for each data set, and these values were used to compute a performance ratio for all daily productivity values. The normalized performance ratios were used in the subsequent analyses.
Statistical analyses used the performance ratios as the dependent variable. The analysis showed that there were strong correlations between the performance ratios and the presence of changes, rework, and disruptions. Using analysis of variance and multiple regression techniques, the average loss of efficiency when work was performed on changes was about 30 percent. Some changes showed no loss of efficiency.
There was a noticeable increase in the percentage of disrupted days when changes work was done. Disruptions were further analyzed by type. Significant increases in
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occurrence and losses of efficiency were observed for material and information availability and out-of-sequence work.
Based on the judgment of the Changes Impact Task Force, one of the significant factors leading to losses of efficiency is the timing of the change. Three cases based on timing are presented. A conceptual model was developed to estimate the impact of a change. The model is based on the how the timing of the change leads to the presence of disruptions. A hypothetical example is presented showing impacts ranging from no impact to a 35 percent loss of efficiency. The losses are very much a function of the scope and duration of the change and the presence of disruptions.
CONCLUSIONS
Based on this research, several conclusions can be stated. These are:
o The effect of construction on labor efficiency is documented. On average, there is a 30 percent loss of efficiency when changes are being performed, although it is possible to perform some changes without a loss of efficiency. The key variable is believed to be the timing of the change.
o Lower labor performance is strongly related to the presence of changes work, disruptions, and rework. The correlation of changes to rework is probably due to difficulties in distinguishing rework from changes. No clear distinction was made in the data collection procedures manual.
o As changes work is performed and performance degrades, there is a 30 percent increase in the number of disrupted days. The most significant types of disruptions are the lack of materials and information and having to perform the work out-of-sequence. These disruptions result in a daily loss of efficiency in the range of 26 to 47 percent.
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o Because disruptions are correlated to losses of efficiency andchanges work is correlated to an increased occurrence of disruptions, it is concluded that changes work causes disruptions and the disruptions are the de facto cause of loss of efficiency. Thus to manage changes work for improved efficiency, it is important to avoid disruptions. Based on the data in this report, lack of materials is the most serious disruption.
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REFERENCES
Brown, D. W., Influences on the Cost of Construction Changes and Forward Change Pricing Report, (Greenville, S.C.: Construction Education Institute, the Frisby Group, 1988).
Dellon, A. L., and Dellon, I. J., "Documentation and Verification in the Change Order Process," Transactions, American Association of Cost Engineers, pp. C.7.1-C.7.5, 1988.
Dieterle, R. A., and Maziarz, S. J., "Substantiation of Costs in Construction Claims," Cose Engineering, Vol. 33, No. 3, pp. 9-12, March 1991.
Hester, W. T., Kuprenas, J. A., and Chang, T. C., Changes During Construction and Their Impact: A Review of the Literature and Development of Management Techniques, Draft Report to the Construction Industry Institute, p. 26, March 1988.
Construction Industry Institute, The Impact of Changes on Construction Cost and Schedule, Report Prepared by the Cost/Schedule Task Force, Austin, TX, p. 21, October 1988.
Leonard, C. A., The Effect of Change Orders on Productivity, The Revay Report, No. 6(2):1-3 (Montreal, Canada: Revay and Associates, Ltd., 1987).
Sanders, S. R., and Thomas, H. R., "Factors Affecting Masonry Productivity," J. Construction Engineering and Management, 117(4):626-644, American Society of Civil Engineers, 1991.
Thomas, H. R., Smith, G. R., Sanders, S. R., and Mannering, F. L., An Exploratory Study of Productivity Forecasting Using the Factor Model for Masonry, Report No. 9005, (University Park, PA: The Pennsylvania State University, Pennsylvania Transportation Institute, p. 155, December 1989).
Thomas, H. R., Maloney, W. F., Smith, G. R., Sanders, S. R., Horner, R. M. W., Handa, V. K., "Productivity Models for Construction," Journal of Construction Engineering and Management, 116(4):705-726, American Society of Civil Engineers, 1990.
Thomas, H. R., Procedures Manual For Collecting Productivity and Related Data on the Effects of Changes on Industrial Construction Projects: Electrical and Piping, Final Report, (University Park, PA: The Pennsylvania State University, Pennsylvania Transportation Institute, June 1991).
United Nations, Effect of Repetition on Building Operations and Processes on Site, United Nations Committee on Housing, Building, and Planning, ST/ECE/HOU/14 (New York, NY: United Nations, 1965). Yanoviak, J. J., "Improving Project Management for Claims Reduction," Transactions, American Association of Cost Engineers, pp. H.2.1- H.2.5, 1987.
Zietoun, A. A., and Oberlender, G. D., Early Warning Signs of Project Changes, Source Document 91, Construction Industry Institute, Austin, TX, p. 111, 1993.
Zink, Dwight A., "Impacts and Construction Inefficiency," Cost Engineering, 32(11), November 1990.
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APPENDIX CII CHANGE ORDER IMPACTS
CII Change Order Impacts
SIZE AND SCOPEA 1 Size and Scope of Change
It is essential that the full scope of the change be understood. The scope includes not only the products to be delivered, but any contractual features which effect the way the change is to be accomplished. A large change order that includes several disciplines can be more disruptive to a project than a change involving a single craft or discipline.
A 11 Plan DetailsThe plan details and drawings are the graphic and pictorials documents that show the design, location and dimensions of the element of the change. Do these fully describe the work to be done.
A 20 How well the Change is DefinedIs there a written description? Are there detailed plans and specifications?
A 32 Planning of Actual CO WorkBy its nature a change order disrupts the planned work. The planning of a change order should include the who, what, why, when and where for the effort. What is the impact of the change on ongoing and future work? Could the change work cause another change order)
A/B 19 Complexity of a Change / Learning Curve The complexity of a change involves the intricacy of the work to be done. Is the change straight forward and similar to existing work? Or is the change unique requiring special skill and equipment?
A/C 16 Procurement / Materials Handling Consideration should be giving to whether the change involves additional procurement of equipment or materials. Will special handling and storage be required? Will these items will affect the timing of the change?
NATURE OF THE CHANGEB 12 Other Work in Plant Area
Will the change effect other work in the plant area? Will the change effect work outside the plant?
B 14 Safety ConsiderationsDoes the change improve the general safety of the project? Doesthe change require addition safety awareness duringconstruction? What precautions must be taken?
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B 23 Cost of the ChangeHave all of the costs of the change been identified? Are there allowances for owner and contractor administrative support? Are there any outside costs such as additional security, fire protection etc.?
B 29 Demolition / RemobilizationDoes the change require demolition of existing work or facilities? Will remobilization of crews be required?
B 3 Details of ChangeSee A 1, A 11, A 20.
B 30 Moving from Original Work to Change Order Work Does the change require stopping original work to start the change work? How mobilization, transportation and demobilization costs been included in the change? Does this transfer of work result in a time extension?
B 33 Driver Behind the Change (Turnaround time)What is the reason for the change? (What, technology, safety etc.) Have all of the components of the motive for the change been considered (ie: safety, owners preference, omission etc.)?
B 36 Changing Quality StandardsIs the change a result of changing quality standards? Do these new quality standards affect other parts of the work?
B 4 Rework Does the change require rework? What is the reason forthe rework? Does this rework impact the ongoing work?
B 5 Complexity of Site OperationsDo site operations impact the changed work? Are there restrictions such as site access, work hours, crew size etc.?
TIMING C 10 Timing in Project Life Cycles
The cost of a change can be affected by when it occurs in the project life cycle. Can the change be executed early in the project where there is usually less of a cost impact?
C 15 Equipment AvailabilityDoes the change involve the procurement of long lead equipment? The change require the use of special rigging or transportation equipment?
C 2 Timing of the ChangeDoes the timing of the change increase its cost? Could the change be executed at another time and reduce the cost? (ie: night shift or weekend work)
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C 28 OvertimeDoes the change require that the work be executed outside the normal work hours? Is overtime required?
C 38 Access to WorkIs there restricted access to the changed work? Win more time be required to execute the change because of the restrictions?
C 9 Total % Change on ProjectIs this change one of several changes implemented on this project? Is the contractor becoming discontent because of the number of changes?
C Study FatigueDoes the work involve extended overtime that could lead to worker fatigue and loss of productivity?
C Study Contract Time ProvisionsDoes the contract contain time provisions, such as liquidated damages, that could impact the schedule of the change work?
C Study AccelerationThe change work require acceleration of the overall schedule?
C Study Beneficial OccupancyWill the owner have beneficial occupancy during the change work? Will this impact the work to be done?
C Study Contractor CapitalIs the contractor being funded for the change work that maintains his cash flow? Is his cash flow adequate to execute the change work?
C Study Contractor WorkloadDoes the contractor have adequate resources to accomplish the change work without falling behind on the existing work?
C Study Delay TimeIs the contractor being delayed from starting the work do to management impacts?
MANAGEMENT IMPACT
D 21 Site Management & Supervision - Demands on Oversight Time Does the change place additional obligations on the site management and supervision? Is there enough supervision to insure that the work is properly completed?
D 22 Home Office & Site ManagementIs additional home office and site management required toadminister the change? What are the direct and indirect costs ofthis additional support?
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D 25 Contract Type: Lump Sum, Cost Plus, Unit Price Etc.What is the best type of contract for the change? Under which contract type can the risks be best shared?
D 31 Authorization and Process (Complexity & Timing)How complex is the change process? Does it increase the time and cost of the change? Is work being delayed because of review and approval policies?
D 34 General Attitude, Knowledge, Awareness of:
Engineering Home Office Is the engineering home office aware of the status of the project? Have they made regular visits? Is the change compatible with the general design intent?
Construction Management Does the construction manager understand the change? Have they investigated all alternatives and recommended the best solution?
Owner Does the owner understand the full impact of the change in terms of time, cost and disruption? Can the change be better completed by the owner after the original work is complete?
Crafts Do the crafts fully understand the change? Can they plan their work to cause the minimum amount of disruption?
D 35 Turnover of PeopleHas there been a high turnover of people? This includes both craft, supervision and owner's staff. Do all parties understand the history of the project and the impact of the change? Is there a cost for this learning curve?
D 37 Impact on Project Controls System (Schedule and Cost Control ofthe Changes)Can the change be tracked on the existing project controlssystem? With this change tracking require additional support andtime?
D Study Worker MotivationDoes management contribute to the motivation of the constructioncrews? Are there any demotivation factors?
WHO DOES THE CHANGEE 13 Other Work in Plant Area
Will other work in the plant area be disrupted because of the change work? E 26 Total Number and Type of Change on This Craft and Total Project
How have the crafts been affected by the change order work? Has one craft completed most of the change work? Has this craft developed an "attitude" concerning change work?
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E 6 Type of CraftWhat craft will complete the changed work? Are multiple crafts required? Will the work cause craft disputes?
E 7 Number of Crafts on SiteIs there enough of craft on site to complete the change work? Will the change work increase the craft density at the site? Will this lead to inefficiencies?
E 8 Open/Closed ShopWill the change be accomplished by union or non-union crafts? Will special entrances be required? Will additional security be required?
E Study Crew Size Does the work area limit the size of the crew that can complete the work?
SITE CONDITIONS (ENVIRONMENT)
E/F 24 Productivity Impact:What will be the productivity impact on the direct craft? On the indirect craft? On management?
F 17 Impact of Other Work in Area Will the change work effect other work in the area?
F 18 Interface with Current Site OperationsWill current site operations be disrupted during execution of change work? Is the work area limited?
F 27 Site ConditionWhat are the site conditions? What are the weather conditions? What is the location of the change work? Is the site congested?
F Study Weather Will the weather have an impact on the change work? Does the change work occur in weather that is different that the original work? (ie: Is the change work being done in the winter versus the summer.)
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